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Social media data generated from individuals provides a unique opportunity to gain valuable insight on information flow, especially for emergency response. However, the inherent limitations associated to these data (particularly, the spatial bias) restrict its precise application. Existing research on spatial bias correction of social media data mainly face two issues: 1) the geographic extent in target domains may be underestimated, and 2) source elements may be transported within inappropriate distance. In this paper, we take 2013 Boulder, Colorado flood event as a case study, and present a new method called subspace optimal transport (SOT). Our proposed SOT aims at transporting biased tweets from dry to real flooded areas with a relatively close distance. Specifically, a comparison between our newly developed SOT and the traditional optimal transport (OT) and geographic optimal transport (GOT) is performed. Experimental results demonstrate that our new SOT method is able to correct the spatially biased geo-referenced tweets, with high precision and excellent computing performance.

Social media data have shown great potential for disaster response. However, the inherent limitations associated to these data (particu-larly, the spatial bias) restrict its precise application. In this work, we present a new spatial bias correction method based on remote sensing knowledge and spatio-temporal fusion, named locally optimal transport (LOT). Our method is first tested using a case study (2013 Boulder, Colorado flood event). Then, we apply our method to a 2016 Wuhan flood event to test its accuracy in a data deficient region. Our results show that combining remote sensing features and spatio-temporal fusion can help to address problems with a lack of prior data and limited disaster period data. According to the random ground verification points collected from news, pictures and videos, our new LOT method is able to accurately relocate spatially biased social media data to inundated areas, which are dangerous for users.

In this paper, a new system is designed to translate dyeing style. The whole system includes blood smears preparation, image acquisition and segmentation, finally, training a dye style transmission network. In the system, we designed a new network based on Deep Learning for the leukocyte dyeing style translation. The network can automatically extract the features of sample images during training and reduces the influence of lighting, dyeing, and taking a photo by different devices. In addition, we also build two datasets, Base dataset and Target dataset.

According to Diesel Engine ECU drive circuit, the drive current of each components measured with the Active Current Clamp was analyzed. According to the drive current of Solenoid pump valve, the heat dissipation of each component was calculated, and then the thermal simulation model was built and the ECU temperature calculated. In addition, an experiment was done to verify the accuracy of the thermal simulation model at 25°C environment. The experiment shows that simulation balance results is almost the same as experimental balance results, there are no more than 1°C difference between simulation results and experimental results, verifies the accuracy of the thermal simulation model.